Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations18153
Missing cells2711
Missing cells (%)0.7%
Total size in memory3.0 MiB
Average record size in memory176.0 B

Variable types

Text12
Numeric10

Alerts

merch_zipcode has 2711 (14.9%) missing values Missing
trans_num has unique values Unique
is_fraud has 18049 (99.4%) zeros Zeros

Reproduction

Analysis started2025-06-27 22:35:37.580316
Analysis finished2025-06-27 22:35:37.950725
Duration0.37 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct693
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:44.732265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length37
Mean length23.10025891
Min length13

Characters and Unicode

Total characters419339
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Doyle Ltd
2nd rowfraud_Leffler-Goldner
3rd rowfraud_Roberts, Ryan and Smith
4th rowfraud_Roob, Conn and Tremblay
5th rowfraud_Gerlach Inc
ValueCountFrequency (%)
and 6616
 
15.6%
llc 1385
 
3.3%
inc 1284
 
3.0%
sons 1045
 
2.5%
ltd 1004
 
2.4%
plc 900
 
2.1%
group 749
 
1.8%
fraud_kutch 147
 
0.3%
fraud_schaefer 145
 
0.3%
fraud_streich 143
 
0.3%
Other values (804) 28860
68.3%
2025-06-27T23:35:45.104470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40627
 
9.7%
r 37657
 
9.0%
d 30050
 
7.2%
u 25956
 
6.2%
e 25951
 
6.2%
n 24758
 
5.9%
24125
 
5.8%
f 19532
 
4.7%
_ 18153
 
4.3%
o 15818
 
3.8%
Other values (45) 156712
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 419339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 40627
 
9.7%
r 37657
 
9.0%
d 30050
 
7.2%
u 25956
 
6.2%
e 25951
 
6.2%
n 24758
 
5.9%
24125
 
5.8%
f 19532
 
4.7%
_ 18153
 
4.3%
o 15818
 
3.8%
Other values (45) 156712
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 419339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 40627
 
9.7%
r 37657
 
9.0%
d 30050
 
7.2%
u 25956
 
6.2%
e 25951
 
6.2%
n 24758
 
5.9%
24125
 
5.8%
f 19532
 
4.7%
_ 18153
 
4.3%
o 15818
 
3.8%
Other values (45) 156712
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 419339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 40627
 
9.7%
r 37657
 
9.0%
d 30050
 
7.2%
u 25956
 
6.2%
e 25951
 
6.2%
n 24758
 
5.9%
24125
 
5.8%
f 19532
 
4.7%
_ 18153
 
4.3%
o 15818
 
3.8%
Other values (45) 156712
37.4%
Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:45.424319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.53897427
Min length4

Characters and Unicode

Total characters191314
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrocery_pos
2nd rowpersonal_care
3rd rowpersonal_care
4th rowshopping_pos
5th rowshopping_net
ValueCountFrequency (%)
gas_transport 1906
10.5%
grocery_pos 1733
9.5%
home 1699
9.4%
shopping_pos 1607
8.9%
kids_pets 1537
8.5%
shopping_net 1397
7.7%
entertainment 1304
 
7.2%
food_dining 1262
 
7.0%
personal_care 1243
 
6.8%
health_fitness 1193
 
6.6%
Other values (4) 3272
18.0%
2025-06-27T23:35:45.608913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 20071
10.5%
e 17929
9.4%
o 17296
9.0%
n 16728
8.7%
p 15204
 
7.9%
t 15146
 
7.9%
_ 14597
 
7.6%
r 12975
 
6.8%
i 11604
 
6.1%
a 9348
 
4.9%
Other values (10) 40416
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 20071
10.5%
e 17929
9.4%
o 17296
9.0%
n 16728
8.7%
p 15204
 
7.9%
t 15146
 
7.9%
_ 14597
 
7.6%
r 12975
 
6.8%
i 11604
 
6.1%
a 9348
 
4.9%
Other values (10) 40416
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 20071
10.5%
e 17929
9.4%
o 17296
9.0%
n 16728
8.7%
p 15204
 
7.9%
t 15146
 
7.9%
_ 14597
 
7.6%
r 12975
 
6.8%
i 11604
 
6.1%
a 9348
 
4.9%
Other values (10) 40416
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 20071
10.5%
e 17929
9.4%
o 17296
9.0%
n 16728
8.7%
p 15204
 
7.9%
t 15146
 
7.9%
_ 14597
 
7.6%
r 12975
 
6.8%
i 11604
 
6.1%
a 9348
 
4.9%
Other values (10) 40416
21.1%

amt
Real number (ℝ)

Distinct9932
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.75698672
Minimum1
Maximum4522.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:45.668295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.78
median47.91
Q383.58
95-th percentile195.338
Maximum4522.11
Range4521.11
Interquartile range (IQR)73.8

Descriptive statistics

Standard deviation121.1801009
Coefficient of variation (CV)1.737175107
Kurtosis186.0266431
Mean69.75698672
Median Absolute Deviation (MAD)37.47
Skewness9.735143691
Sum1266298.58
Variance14684.61685
MonotonicityNot monotonic
2025-06-27T23:35:45.725706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.54 13
 
0.1%
7.11 13
 
0.1%
1.96 12
 
0.1%
4.04 12
 
0.1%
5.12 12
 
0.1%
4.37 12
 
0.1%
2.06 12
 
0.1%
7.51 11
 
0.1%
1.05 11
 
0.1%
2.21 11
 
0.1%
Other values (9922) 18034
99.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
1.01 1
 
< 0.1%
1.02 6
< 0.1%
1.03 7
< 0.1%
1.04 9
< 0.1%
ValueCountFrequency (%)
4522.11 1
< 0.1%
2833.54 1
< 0.1%
2637.33 1
< 0.1%
2280.14 1
< 0.1%
2099.97 1
< 0.1%

first
Text

Distinct337
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:45.937141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.06929984
Min length3

Characters and Unicode

Total characters110176
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDavid
2nd rowEmily
3rd rowJames
4th rowDustin
5th rowLisa
ValueCountFrequency (%)
christopher 346
 
1.9%
david 304
 
1.7%
robert 298
 
1.6%
jessica 292
 
1.6%
michael 284
 
1.6%
james 278
 
1.5%
jennifer 258
 
1.4%
william 253
 
1.4%
john 224
 
1.2%
margaret 222
 
1.2%
Other values (327) 15394
84.8%
2025-06-27T23:35:46.159535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14132
 
12.8%
e 12024
 
10.9%
n 8749
 
7.9%
i 8705
 
7.9%
r 8430
 
7.7%
l 5330
 
4.8%
h 4635
 
4.2%
s 4549
 
4.1%
t 4302
 
3.9%
o 3706
 
3.4%
Other values (39) 35614
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14132
 
12.8%
e 12024
 
10.9%
n 8749
 
7.9%
i 8705
 
7.9%
r 8430
 
7.7%
l 5330
 
4.8%
h 4635
 
4.2%
s 4549
 
4.1%
t 4302
 
3.9%
o 3706
 
3.4%
Other values (39) 35614
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14132
 
12.8%
e 12024
 
10.9%
n 8749
 
7.9%
i 8705
 
7.9%
r 8430
 
7.7%
l 5330
 
4.8%
h 4635
 
4.2%
s 4549
 
4.1%
t 4302
 
3.9%
o 3706
 
3.4%
Other values (39) 35614
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14132
 
12.8%
e 12024
 
10.9%
n 8749
 
7.9%
i 8705
 
7.9%
r 8430
 
7.7%
l 5330
 
4.8%
h 4635
 
4.2%
s 4549
 
4.1%
t 4302
 
3.9%
o 3706
 
3.4%
Other values (39) 35614
32.3%

last
Text

Distinct465
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:46.403374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.109568666
Min length2

Characters and Unicode

Total characters110907
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEverett
2nd rowHall
3rd rowStephens
4th rowRoberts
5th rowLopez
ValueCountFrequency (%)
smith 391
 
2.2%
williams 328
 
1.8%
davis 300
 
1.7%
johnson 295
 
1.6%
rodriguez 242
 
1.3%
martinez 221
 
1.2%
lewis 202
 
1.1%
jones 199
 
1.1%
gonzalez 184
 
1.0%
martin 166
 
0.9%
Other values (455) 15625
86.1%
2025-06-27T23:35:46.609118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11112
 
10.0%
r 9345
 
8.4%
a 9145
 
8.2%
n 8595
 
7.7%
o 8119
 
7.3%
s 6749
 
6.1%
l 6748
 
6.1%
i 6077
 
5.5%
t 4092
 
3.7%
h 3141
 
2.8%
Other values (38) 37784
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11112
 
10.0%
r 9345
 
8.4%
a 9145
 
8.2%
n 8595
 
7.7%
o 8119
 
7.3%
s 6749
 
6.1%
l 6748
 
6.1%
i 6077
 
5.5%
t 4092
 
3.7%
h 3141
 
2.8%
Other values (38) 37784
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11112
 
10.0%
r 9345
 
8.4%
a 9145
 
8.2%
n 8595
 
7.7%
o 8119
 
7.3%
s 6749
 
6.1%
l 6748
 
6.1%
i 6077
 
5.5%
t 4092
 
3.7%
h 3141
 
2.8%
Other values (38) 37784
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11112
 
10.0%
r 9345
 
8.4%
a 9145
 
8.2%
n 8595
 
7.7%
o 8119
 
7.3%
s 6749
 
6.1%
l 6748
 
6.1%
i 6077
 
5.5%
t 4092
 
3.7%
h 3141
 
2.8%
Other values (38) 37784
34.1%

gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:46.642858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18153
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM
5th rowF
ValueCountFrequency (%)
f 9817
54.1%
m 8336
45.9%
2025-06-27T23:35:46.709540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 9817
54.1%
M 8336
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 9817
54.1%
M 8336
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 9817
54.1%
M 8336
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 9817
54.1%
M 8336
45.9%

street
Text

Distinct918
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:46.840014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.12631521
Min length12

Characters and Unicode

Total characters401659
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row4138 David Fall
2nd row8851 Reese Neck
3rd row1166 Castillo Mountains
4th row3283 James Station
5th row32343 Saunders Course
ValueCountFrequency (%)
apt 4504
 
6.3%
suite 4187
 
5.8%
island 328
 
0.5%
michael 283
 
0.4%
common 256
 
0.4%
station 243
 
0.3%
brooks 239
 
0.3%
david 232
 
0.3%
islands 229
 
0.3%
tunnel 221
 
0.3%
Other values (1838) 61119
85.1%
2025-06-27T23:35:47.220476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53688
 
13.4%
e 25101
 
6.2%
a 20261
 
5.0%
i 18026
 
4.5%
t 17170
 
4.3%
r 15336
 
3.8%
n 14918
 
3.7%
s 14514
 
3.6%
l 12451
 
3.1%
o 12249
 
3.0%
Other values (52) 197945
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 401659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
53688
 
13.4%
e 25101
 
6.2%
a 20261
 
5.0%
i 18026
 
4.5%
t 17170
 
4.3%
r 15336
 
3.8%
n 14918
 
3.7%
s 14514
 
3.6%
l 12451
 
3.1%
o 12249
 
3.0%
Other values (52) 197945
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 401659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
53688
 
13.4%
e 25101
 
6.2%
a 20261
 
5.0%
i 18026
 
4.5%
t 17170
 
4.3%
r 15336
 
3.8%
n 14918
 
3.7%
s 14514
 
3.6%
l 12451
 
3.1%
o 12249
 
3.0%
Other values (52) 197945
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 401659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
53688
 
13.4%
e 25101
 
6.2%
a 20261
 
5.0%
i 18026
 
4.5%
t 17170
 
4.3%
r 15336
 
3.8%
n 14918
 
3.7%
s 14514
 
3.6%
l 12451
 
3.1%
o 12249
 
3.0%
Other values (52) 197945
49.3%

city
Text

Distinct843
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:47.473532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.651517655
Min length3

Characters and Unicode

Total characters157051
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowMorrisdale
2nd rowBasye
3rd rowRuckersville
4th rowFayetteville
5th rowDongola
ValueCountFrequency (%)
city 282
 
1.3%
west 251
 
1.1%
saint 201
 
0.9%
falls 199
 
0.9%
north 198
 
0.9%
new 185
 
0.8%
mount 165
 
0.7%
lake 148
 
0.7%
springs 133
 
0.6%
port 120
 
0.5%
Other values (871) 20674
91.7%
2025-06-27T23:35:47.688295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 15256
 
9.7%
a 13073
 
8.3%
n 11773
 
7.5%
o 11382
 
7.2%
l 10925
 
7.0%
r 10452
 
6.7%
i 9785
 
6.2%
t 8408
 
5.4%
s 6254
 
4.0%
4403
 
2.8%
Other values (42) 55340
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15256
 
9.7%
a 13073
 
8.3%
n 11773
 
7.5%
o 11382
 
7.2%
l 10925
 
7.0%
r 10452
 
6.7%
i 9785
 
6.2%
t 8408
 
5.4%
s 6254
 
4.0%
4403
 
2.8%
Other values (42) 55340
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15256
 
9.7%
a 13073
 
8.3%
n 11773
 
7.5%
o 11382
 
7.2%
l 10925
 
7.0%
r 10452
 
6.7%
i 9785
 
6.2%
t 8408
 
5.4%
s 6254
 
4.0%
4403
 
2.8%
Other values (42) 55340
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15256
 
9.7%
a 13073
 
8.3%
n 11773
 
7.5%
o 11382
 
7.2%
l 10925
 
7.0%
r 10452
 
6.7%
i 9785
 
6.2%
t 8408
 
5.4%
s 6254
 
4.0%
4403
 
2.8%
Other values (42) 55340
35.2%

state
Text

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:47.788631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36306
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPA
2nd rowVA
3rd rowVA
4th rowNC
5th rowIL
ValueCountFrequency (%)
tx 1267
 
7.0%
ny 1186
 
6.5%
pa 1057
 
5.8%
ca 830
 
4.6%
oh 709
 
3.9%
al 649
 
3.6%
il 586
 
3.2%
mi 574
 
3.2%
fl 567
 
3.1%
mo 550
 
3.0%
Other values (40) 10178
56.1%
2025-06-27T23:35:47.931198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5090
14.0%
N 3997
 
11.0%
M 3053
 
8.4%
I 2485
 
6.8%
O 2116
 
5.8%
T 2105
 
5.8%
L 2085
 
5.7%
C 1969
 
5.4%
Y 1802
 
5.0%
W 1336
 
3.7%
Other values (14) 10268
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5090
14.0%
N 3997
 
11.0%
M 3053
 
8.4%
I 2485
 
6.8%
O 2116
 
5.8%
T 2105
 
5.8%
L 2085
 
5.7%
C 1969
 
5.4%
Y 1802
 
5.0%
W 1336
 
3.7%
Other values (14) 10268
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5090
14.0%
N 3997
 
11.0%
M 3053
 
8.4%
I 2485
 
6.8%
O 2116
 
5.8%
T 2105
 
5.8%
L 2085
 
5.7%
C 1969
 
5.4%
Y 1802
 
5.0%
W 1336
 
3.7%
Other values (14) 10268
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5090
14.0%
N 3997
 
11.0%
M 3053
 
8.4%
I 2485
 
6.8%
O 2116
 
5.8%
T 2105
 
5.8%
L 2085
 
5.7%
C 1969
 
5.4%
Y 1802
 
5.0%
W 1336
 
3.7%
Other values (14) 10268
28.3%

zip
Real number (ℝ)

Distinct907
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48848.2523
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:47.984419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126292
median48088
Q372011
95-th percentile94971
Maximum99783
Range98526
Interquartile range (IQR)45719

Descriptive statistics

Standard deviation26916.12884
Coefficient of variation (CV)0.5510151862
Kurtosis-1.08346995
Mean48848.2523
Median Absolute Deviation (MAD)22875
Skewness0.08695758825
Sum886742324
Variance724477991.6
MonotonicityNot monotonic
2025-06-27T23:35:48.048279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39073 55
 
0.3%
36749 54
 
0.3%
44233 54
 
0.3%
59448 54
 
0.3%
80120 51
 
0.3%
66018 50
 
0.3%
92585 50
 
0.3%
76578 49
 
0.3%
73034 49
 
0.3%
73754 48
 
0.3%
Other values (897) 17639
97.2%
ValueCountFrequency (%)
1257 26
0.1%
1330 14
0.1%
1535 6
 
< 0.1%
1545 17
0.1%
1612 7
 
< 0.1%
ValueCountFrequency (%)
99783 22
0.1%
99746 7
 
< 0.1%
99323 31
0.2%
99160 42
0.2%
99116 1
 
< 0.1%

lat
Real number (ℝ)

Distinct905
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.56127203
Minimum20.0271
Maximum65.6899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:48.151685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.9912
Q134.6205
median39.39
Q341.9404
95-th percentile45.8327
Maximum65.6899
Range45.6628
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.060958617
Coefficient of variation (CV)0.1312445972
Kurtosis0.853368198
Mean38.56127203
Median Absolute Deviation (MAD)3.3656
Skewness-0.2007970779
Sum700002.7712
Variance25.61330212
MonotonicityNot monotonic
2025-06-27T23:35:48.226536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.153 55
 
0.3%
41.2419 54
 
0.3%
48.2777 54
 
0.3%
32.5104 54
 
0.3%
39.5994 51
 
0.3%
38.9462 50
 
0.3%
33.7467 50
 
0.3%
30.592 49
 
0.3%
35.6665 49
 
0.3%
48.6031 48
 
0.3%
Other values (895) 17639
97.2%
ValueCountFrequency (%)
20.0271 25
0.1%
20.0827 22
0.1%
24.6557 24
0.1%
26.1184 38
0.2%
26.3304 7
 
< 0.1%
ValueCountFrequency (%)
65.6899 7
 
< 0.1%
64.7556 22
0.1%
48.8878 42
0.2%
48.8856 26
0.1%
48.8328 22
0.1%

long
Real number (ℝ)

Distinct906
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.36908345
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative18153
Negative (%)100.0%
Memory size141.9 KiB
2025-06-27T23:35:48.325013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-120.0936
Q1-96.8094
median-87.5917
Q3-80.2099
95-th percentile-73.5365
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.5995

Descriptive statistics

Standard deviation13.92234478
Coefficient of variation (CV)-0.1540609271
Kurtosis1.945018795
Mean-90.36908345
Median Absolute Deviation (MAD)8.1106
Skewness-1.17511468
Sum-1640469.972
Variance193.8316841
MonotonicityNot monotonic
2025-06-27T23:35:48.458561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-90.1217 55
 
0.3%
-81.7453 54
 
0.3%
-86.8138 54
 
0.3%
-112.8456 54
 
0.3%
-105.0044 51
 
0.3%
-94.9714 50
 
0.3%
-117.1721 50
 
0.3%
-97.2893 49
 
0.3%
-97.4798 49
 
0.3%
-98.0727 48
 
0.3%
Other values (896) 17639
97.2%
ValueCountFrequency (%)
-165.6723 22
0.1%
-156.292 7
 
< 0.1%
-155.488 22
0.1%
-155.3697 25
0.1%
-124.4409 11
0.1%
ValueCountFrequency (%)
-67.9503 34
0.2%
-68.5565 11
 
0.1%
-69.2675 5
 
< 0.1%
-69.4828 27
0.1%
-69.9576 10
 
0.1%

city_pop
Real number (ℝ)

Distinct829
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86514.21324
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:48.597606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile140
Q1743
median2443
Q319685
95-th percentile493806
Maximum2906700
Range2906677
Interquartile range (IQR)18942

Descriptive statistics

Standard deviation298803.9021
Coefficient of variation (CV)3.453812858
Kurtosis39.1478698
Mean86514.21324
Median Absolute Deviation (MAD)2189
Skewness5.721151599
Sum1570492513
Variance8.928377191 × 1010
MonotonicityNot monotonic
2025-06-27T23:35:48.825752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 84
 
0.5%
1312922 73
 
0.4%
198 70
 
0.4%
241 62
 
0.3%
1766 62
 
0.3%
237282 61
 
0.3%
743 61
 
0.3%
1595797 57
 
0.3%
2135 57
 
0.3%
190178 57
 
0.3%
Other values (819) 17509
96.5%
ValueCountFrequency (%)
23 24
0.1%
37 14
 
0.1%
43 26
0.1%
46 41
0.2%
47 4
 
< 0.1%
ValueCountFrequency (%)
2906700 56
0.3%
2504700 33
0.2%
2383912 7
 
< 0.1%
1595797 57
0.3%
1577385 42
0.2%

job
Text

Distinct477
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:49.124180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.2053655
Min length3

Characters and Unicode

Total characters366788
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAdvice worker
2nd rowEngineer, mining
3rd rowDesigner, ceramics/pottery
4th rowScientist, research (maths)
5th rowScientific laboratory technician
ValueCountFrequency (%)
engineer 1882
 
4.7%
officer 1470
 
3.6%
manager 864
 
2.1%
scientist 756
 
1.9%
designer 712
 
1.8%
surveyor 679
 
1.7%
teacher 542
 
1.3%
psychologist 475
 
1.2%
public 409
 
1.0%
research 402
 
1.0%
Other values (448) 32152
79.7%
2025-06-27T23:35:49.848778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39465
 
10.8%
i 33416
 
9.1%
r 30683
 
8.4%
a 25634
 
7.0%
t 24948
 
6.8%
n 24723
 
6.7%
22190
 
6.0%
o 20569
 
5.6%
s 20158
 
5.5%
c 18448
 
5.0%
Other values (43) 106554
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39465
 
10.8%
i 33416
 
9.1%
r 30683
 
8.4%
a 25634
 
7.0%
t 24948
 
6.8%
n 24723
 
6.7%
22190
 
6.0%
o 20569
 
5.6%
s 20158
 
5.5%
c 18448
 
5.0%
Other values (43) 106554
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39465
 
10.8%
i 33416
 
9.1%
r 30683
 
8.4%
a 25634
 
7.0%
t 24948
 
6.8%
n 24723
 
6.7%
22190
 
6.0%
o 20569
 
5.6%
s 20158
 
5.5%
c 18448
 
5.0%
Other values (43) 106554
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39465
 
10.8%
i 33416
 
9.1%
r 30683
 
8.4%
a 25634
 
7.0%
t 24948
 
6.8%
n 24723
 
6.7%
22190
 
6.0%
o 20569
 
5.6%
s 20158
 
5.5%
c 18448
 
5.0%
Other values (43) 106554
29.1%

trans_num
Text

Unique 

Distinct18153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:50.050503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters580896
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18153 ?
Unique (%)100.0%

Sample

1st rowf487a7098c0bd4d45f710be1745c4acb
2nd rowc2ed76f03cce8a6b362729a5a23f01c2
3rd row3c1a5b82410aa0824f7f4aaace067fc8
4th rowb94caace2f48b8b97a21add8661dca48
5th row4b64dbdb7526ac8693b886fb85508d83
ValueCountFrequency (%)
f487a7098c0bd4d45f710be1745c4acb 1
 
< 0.1%
08738d8452c88b56a77ff1e606602808 1
 
< 0.1%
dd8ad4284d7011ff096667a0b95f5817 1
 
< 0.1%
8ac117196602127c410b44961240e74e 1
 
< 0.1%
9a927c2398b6e5eb827554f7cc1e1e44 1
 
< 0.1%
e68f672e052f723afc6d6db0b532dee0 1
 
< 0.1%
0aeb156831f79d61ef266d328bd29c46 1
 
< 0.1%
c28ee701de7045a26a7499788bc1170b 1
 
< 0.1%
5dcbe89dfbaa26ea8afaf29d62bf196a 1
 
< 0.1%
b94caace2f48b8b97a21add8661dca48 1
 
< 0.1%
Other values (18143) 18143
99.9%
2025-06-27T23:35:50.288201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 36697
 
6.3%
9 36497
 
6.3%
3 36437
 
6.3%
d 36434
 
6.3%
7 36427
 
6.3%
4 36413
 
6.3%
5 36392
 
6.3%
1 36335
 
6.3%
8 36290
 
6.2%
e 36231
 
6.2%
Other values (6) 216743
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 580896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 36697
 
6.3%
9 36497
 
6.3%
3 36437
 
6.3%
d 36434
 
6.3%
7 36427
 
6.3%
4 36413
 
6.3%
5 36392
 
6.3%
1 36335
 
6.3%
8 36290
 
6.2%
e 36231
 
6.2%
Other values (6) 216743
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 580896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 36697
 
6.3%
9 36497
 
6.3%
3 36437
 
6.3%
d 36434
 
6.3%
7 36427
 
6.3%
4 36413
 
6.3%
5 36392
 
6.3%
1 36335
 
6.3%
8 36290
 
6.2%
e 36231
 
6.2%
Other values (6) 216743
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 580896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 36697
 
6.3%
9 36497
 
6.3%
3 36437
 
6.3%
d 36434
 
6.3%
7 36427
 
6.3%
4 36413
 
6.3%
5 36392
 
6.3%
1 36335
 
6.3%
8 36290
 
6.2%
e 36231
 
6.2%
Other values (6) 216743
37.3%

merch_lat
Real number (ℝ)

Distinct18142
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.55964191
Minimum19.063792
Maximum66.213564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:50.348858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.063792
5-th percentile29.8033422
Q134.741658
median39.41096
Q341.946404
95-th percentile46.0574962
Maximum66.213564
Range47.149772
Interquartile range (IQR)7.204746

Descriptive statistics

Standard deviation5.095965845
Coefficient of variation (CV)0.1321580179
Kurtosis0.8488428112
Mean38.55964191
Median Absolute Deviation (MAD)3.388456
Skewness-0.1971405132
Sum699973.1796
Variance25.96886789
MonotonicityNot monotonic
2025-06-27T23:35:50.405577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.480752 2
 
< 0.1%
41.64297 2
 
< 0.1%
41.229733 2
 
< 0.1%
43.619733 2
 
< 0.1%
35.358278 2
 
< 0.1%
41.491069 2
 
< 0.1%
41.726779 2
 
< 0.1%
26.654523 2
 
< 0.1%
37.674944 2
 
< 0.1%
43.494756 2
 
< 0.1%
Other values (18132) 18133
99.9%
ValueCountFrequency (%)
19.063792 1
< 0.1%
19.132314 1
< 0.1%
19.142126 1
< 0.1%
19.205749 1
< 0.1%
19.209212 1
< 0.1%
ValueCountFrequency (%)
66.213564 1
< 0.1%
65.938166 1
< 0.1%
65.931498 1
< 0.1%
65.732767 1
< 0.1%
65.711358 1
< 0.1%

merch_long
Real number (ℝ)

Distinct18151
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.36667849
Minimum-166.661968
Maximum-67.013198
Zeros0
Zeros (%)0.0%
Negative18153
Negative (%)100.0%
Memory size141.9 KiB
2025-06-27T23:35:50.459659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.661968
5-th percentile-119.91226
Q1-96.837085
median-87.569114
Q3-80.301439
95-th percentile-73.3850064
Maximum-67.013198
Range99.64877
Interquartile range (IQR)16.535646

Descriptive statistics

Standard deviation13.93251466
Coefficient of variation (CV)-0.1541775673
Kurtosis1.929297724
Mean-90.36667849
Median Absolute Deviation (MAD)8.225695
Skewness-1.172191225
Sum-1640426.315
Variance194.1149646
MonotonicityNot monotonic
2025-06-27T23:35:50.524207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110.75373 2
 
< 0.1%
-79.970069 2
 
< 0.1%
-87.649197 1
 
< 0.1%
-87.990435 1
 
< 0.1%
-82.322676 1
 
< 0.1%
-83.764971 1
 
< 0.1%
-107.629896 1
 
< 0.1%
-97.484688 1
 
< 0.1%
-109.59304 1
 
< 0.1%
-98.305875 1
 
< 0.1%
Other values (18141) 18141
99.9%
ValueCountFrequency (%)
-166.661968 1
< 0.1%
-166.65656 1
< 0.1%
-166.25164 1
< 0.1%
-166.194748 1
< 0.1%
-166.154588 1
< 0.1%
ValueCountFrequency (%)
-67.013198 1
< 0.1%
-67.084269 1
< 0.1%
-67.200802 1
< 0.1%
-67.225983 1
< 0.1%
-67.435276 1
< 0.1%

is_fraud
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005729080593
Minimum0
Maximum1
Zeros18049
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:50.584586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0754756387
Coefficient of variation (CV)13.17412759
Kurtosis169.6008802
Mean0.005729080593
Median Absolute Deviation (MAD)0
Skewness13.09893868
Sum104
Variance0.005696572037
MonotonicityNot monotonic
2025-06-27T23:35:50.632440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 18049
99.4%
1 104
 
0.6%
ValueCountFrequency (%)
0 18049
99.4%
1 104
 
0.6%
ValueCountFrequency (%)
1 104
 
0.6%
0 18049
99.4%

merch_zipcode
Real number (ℝ)

Missing 

Distinct10537
Distinct (%)68.2%
Missing2711
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean46813.36725
Minimum1003
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:50.685196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile7806.75
Q125523.25
median45660
Q367849
95-th percentile93243
Maximum99403
Range98400
Interquartile range (IQR)42325.75

Descriptive statistics

Standard deviation25775.03
Coefficient of variation (CV)0.5505912416
Kurtosis-0.9661572482
Mean46813.36725
Median Absolute Deviation (MAD)21195.5
Skewness0.1642840597
Sum722892017
Variance664352171.3
MonotonicityNot monotonic
2025-06-27T23:35:50.768527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34266 9
 
< 0.1%
39180 9
 
< 0.1%
44004 8
 
< 0.1%
21824 7
 
< 0.1%
16249 7
 
< 0.1%
20135 7
 
< 0.1%
43412 7
 
< 0.1%
35619 6
 
< 0.1%
31087 6
 
< 0.1%
76050 6
 
< 0.1%
Other values (10527) 15370
84.7%
(Missing) 2711
 
14.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1007 2
< 0.1%
1011 2
< 0.1%
1031 2
< 0.1%
1036 3
< 0.1%
ValueCountFrequency (%)
99403 1
< 0.1%
99401 1
< 0.1%
99371 2
< 0.1%
99360 1
< 0.1%
99354 1
< 0.1%
Distinct18151
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:50.967264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters344907
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18149 ?
Unique (%)> 99.9%

Sample

1st row2019-09-19 07:09:46
2nd row2019-02-04 20:37:44
3rd row2020-06-05 12:46:32
4th row2019-12-16 22:21:07
5th row2019-12-08 13:29:27
ValueCountFrequency (%)
2019-12-08 102
 
0.3%
2019-12-15 99
 
0.3%
2019-12-01 94
 
0.3%
2019-12-09 93
 
0.3%
2019-12-23 86
 
0.2%
2019-12-22 83
 
0.2%
2019-12-02 82
 
0.2%
2019-12-16 81
 
0.2%
2019-11-30 79
 
0.2%
2019-12-29 77
 
0.2%
Other values (16821) 35430
97.6%
2025-06-27T23:35:51.184536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63605
18.4%
2 49881
14.5%
1 48030
13.9%
- 36306
10.5%
: 36306
10.5%
9 20781
 
6.0%
18153
 
5.3%
3 16861
 
4.9%
4 14837
 
4.3%
5 14824
 
4.3%
Other values (3) 25323
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 344907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 63605
18.4%
2 49881
14.5%
1 48030
13.9%
- 36306
10.5%
: 36306
10.5%
9 20781
 
6.0%
18153
 
5.3%
3 16861
 
4.9%
4 14837
 
4.3%
5 14824
 
4.3%
Other values (3) 25323
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 344907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 63605
18.4%
2 49881
14.5%
1 48030
13.9%
- 36306
10.5%
: 36306
10.5%
9 20781
 
6.0%
18153
 
5.3%
3 16861
 
4.9%
4 14837
 
4.3%
5 14824
 
4.3%
Other values (3) 25323
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 344907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 63605
18.4%
2 49881
14.5%
1 48030
13.9%
- 36306
10.5%
: 36306
10.5%
9 20781
 
6.0%
18153
 
5.3%
3 16861
 
4.9%
4 14837
 
4.3%
5 14824
 
4.3%
Other values (3) 25323
 
7.3%
Distinct918
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:51.365656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1161792
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row80615291af80e886b6ab96bdd0adb5b84795fe9c959c8774bf0a612025fc3978
2nd rowc7b0882e51a8cc4fedd2d30e5600bd92cccfcc6decdcd4705b8ecfea8fa69a53
3rd row2eb1fd42c708d51567ba3a4e8500b02c1e8c39c9c70d5fb418a70cf5dba01ef0
4th row0b1ae26a23f6188ab863a55cc931920d69eed0a1a8b66488a1444f729d02e699
5th row317dd4d30853ffac06fad84be4c1b43bdeeb3be91b402978f9fee77529201692
ValueCountFrequency (%)
5b9e1e38487f8570c6ec1f33df8fe20aef8ec38a7a3329edd84614fec3a39be3 55
 
0.3%
a51fcb4f97c466a4aa8faabeebf0c91a6d5b20d8162ddb8cd430c49dc3ba6c87 54
 
0.3%
3f128965b93d1c1772d7a27473f11a25dce7bd0def5883e3f86eeb3691d3fae4 54
 
0.3%
3ca14369f59c56e79d6bad9db761ad082be4dcb4a1935edbc04fe81c3a1487bf 54
 
0.3%
5d1b3fd0878e831cd01c043aa46b99650e48f258dbad07c6e15e0532f005672b 51
 
0.3%
6a3f2c3d9ef0ffae189e7fd32ac858d1c41cd14921f33d52c277828b711ca73a 50
 
0.3%
f1a9eb73c5421df85abb7d2a9429788273afa28826a47fc2140ca0d2014533a4 50
 
0.3%
e8ea2c5345495b65ee7b4863eaa23dbb32b202d8135cd5e41e292d2c7441add6 49
 
0.3%
e12dd38ac295a4e34061d6aa606fe9302235ecbe2af45367df8e2307bde2cd57 49
 
0.3%
5a64e8fe49586fa2d1dfebf765bf85596adb2f5a45a58300772c4538d780d5d5 48
 
0.3%
Other values (908) 17639
97.2%
2025-06-27T23:35:51.616271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 75422
 
6.5%
2 74443
 
6.4%
e 74118
 
6.4%
1 73978
 
6.4%
7 73935
 
6.4%
3 73515
 
6.3%
f 73110
 
6.3%
5 73082
 
6.3%
d 72948
 
6.3%
4 72566
 
6.2%
Other values (6) 424675
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1161792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 75422
 
6.5%
2 74443
 
6.4%
e 74118
 
6.4%
1 73978
 
6.4%
7 73935
 
6.4%
3 73515
 
6.3%
f 73110
 
6.3%
5 73082
 
6.3%
d 72948
 
6.3%
4 72566
 
6.2%
Other values (6) 424675
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1161792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 75422
 
6.5%
2 74443
 
6.4%
e 74118
 
6.4%
1 73978
 
6.4%
7 73935
 
6.4%
3 73515
 
6.3%
f 73110
 
6.3%
5 73082
 
6.3%
d 72948
 
6.3%
4 72566
 
6.2%
Other values (6) 424675
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1161792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 75422
 
6.5%
2 74443
 
6.4%
e 74118
 
6.4%
1 73978
 
6.4%
7 73935
 
6.4%
3 73515
 
6.3%
f 73110
 
6.3%
5 73082
 
6.3%
d 72948
 
6.3%
4 72566
 
6.2%
Other values (6) 424675
36.6%

age
Real number (ℝ)

Distinct83
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.29025505
Minimum13
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size141.9 KiB
2025-06-27T23:35:51.671830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile21
Q132
median43
Q356
95-th percentile79
Maximum95
Range82
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.22034572
Coefficient of variation (CV)0.3802218755
Kurtosis-0.1463242252
Mean45.29025505
Median Absolute Deviation (MAD)12
Skewness0.6134174717
Sum822154
Variance296.5403066
MonotonicityNot monotonic
2025-06-27T23:35:51.726566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 571
 
3.1%
35 524
 
2.9%
34 507
 
2.8%
43 477
 
2.6%
44 475
 
2.6%
46 465
 
2.6%
33 462
 
2.5%
31 458
 
2.5%
32 433
 
2.4%
45 426
 
2.3%
Other values (73) 13355
73.6%
ValueCountFrequency (%)
13 2
 
< 0.1%
14 59
0.3%
15 84
0.5%
16 46
0.3%
17 33
 
0.2%
ValueCountFrequency (%)
95 3
 
< 0.1%
94 3
 
< 0.1%
93 56
0.3%
92 57
0.3%
91 65
0.4%